Does creatine cause hair loss? A 12-week randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Creatine is a widely used ergogenic aid that enhances muscle strength and lean mass. However, concerns have been raised about the potential role in promoting hair loss by increasing dihydrotestosterone (DHT). Currently, there is no direct evidence examining the relationship between creatine supplementation and hair follicle health. Therefore, the purpose was to determine the effects of 12 weeks of creatine supplementation on androgen levels and hair follicle health in healthy young males. METHODS: Forty-five resistance-trained males (ages 18-40 years) were recruited and randomly assigned to either a creatine monohydrate (5 g/day) or placebo (5 g maltodextrin/day) group. Participants maintained their habitual diets and training routines. Blood samples were collected at baseline and after 12 weeks to measure total testosterone, free testosterone, and DHT. Hair follicle health was assessed using the Trichogram test and the FotoFinder system (hair density, follicular unit count, and cumulative hair thickness). Statistical analyses were performed using repeated measures ANOVA, and potential outliers were examined through sensitivity analysis. RESULTS: > 0.05). While total testosterone increased (∆ = post value minus pre value: creatine = ∆124 ± 149 ng/dL; placebo = ∆216 ± 203 ng/dL) and free testosterone decreased (creatine = ∆-9.0 ± 8.7 pg/mL; placebo = ∆-9 ± 6.4 pg/mL) over time, these effects were independent of supplementation. There were no significant differences in DHT levels, DHT-to-testosterone ratio, or hair growth parameters between the creatine and placebo groups. CONCLUSION: This study was the first to directly assess hair follicle health following creatine supplementation, providing strong evidence against the claim that creatine contributes to hair loss.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".